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Deterministic Learning Theory for Identification, Recognition, and Control

Cong Wang | David J. Hill
€254.20
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A01=Cong Wang
A01=David J. Hill
Adaptive NN Control
advanced control theory
Author_Cong Wang
Author_David J. Hill
Brunovsky Form
Category=UYQE
closed-loop neural control
David J. Hill
deterministic learning theory
Direct Adaptive NN Control
Dl
Duffing Oscillator
dynamical parallel distributed processing model
Dynamical Pattern
dynamical patterns
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eq_computing
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eq_isMigrated=2
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Estimated State Trajectory
Exponential Stability
fault detection algorithms
High Gain Observer
Human-Like Learning
Knowledge Acquisition
LTV System
neural control methods
neural network learning in dynamic systems
Neural Weights
NN Approximation
NN Approximation Error
NN Input
Nonlinear Observer Design
nonlinear system modeling
Observation Error
pattern-based learning
Pe Condition
radial basis function networks
RBF Approximation
RBF Network
RBF Network Model
State Observation Error
Strict Feedback Systems
system identification
temporal pattern recognition
UGES
Unknown Smooth Nonlinear Function
Van Der Pol Oscillator

Product details

  • ISBN 9780849375538
  • Weight: 514g
  • Dimensions: 156 x 234mm
  • Publication Date: 21 Jul 2009
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description

Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments. It provides systematic design approaches for identification, recognition, and control of linear uncertain systems. Unlike many books currently available that focus on statistical principles, this book stresses learning through closed-loop neural control, effective representation and recognition of temporal patterns in a deterministic way.

A Deterministic View of Learning in Dynamic Environments

The authors begin with an introduction to the concepts of deterministic learning theory, followed by a discussion of the persistent excitation property of RBF networks. They describe the elements of deterministic learning, and address dynamical pattern recognition and pattern-based control processes. The results are applicable to areas such as detection and isolation of oscillation faults, ECG/EEG pattern recognition, robot learning and control, and security analysis and control of power systems.

A New Model of Information Processing

This book elucidates a learning theory which is developed using concepts and tools from the discipline of systems and control. Fundamental knowledge about system dynamics is obtained from dynamical processes, and is then utilized to achieve rapid recognition of dynamical patterns and pattern-based closed-loop control via the so-called internal and dynamical matching of system dynamics. This actually represents a new model of information processing, i.e. a model of dynamical parallel distributed processing (DPDP).
About Cong Wang | David J. Hill
National Natural Science Foundation of China, Haidian, Beiji The University of Texas at Arlington, USA

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